Generative AI systems and AI agents behave differently from traditional predictive models. They produce open-ended outputs, interact with tools, and often maintain context across turns. Auditing them requires adapted methods.

What Generative and Agent Audits Evaluate

Structured reviews typically assess:

• Response quality, consistency, and factual grounding
• Retrieval pipeline reliability for knowledge-based systems
• Tool use accuracy and permission boundaries for agents
• Handling of edge cases, adversarial inputs, and sensitive topics
• Logging, traceability, and human oversight mechanisms
• Cost, latency, and operational stability

These dimensions determine whether the system is ready for broader or higher-stakes use.

Specific Risks in Generative and Agent Systems

Hallucinations, unauthorized actions, prompt injection, and uncontrolled tool use are common concerns. Audits help surface whether existing guardrails, monitoring, and escalation paths are effective under realistic conditions.

Improving Reliability Through Assessment

Findings often lead to stronger retrieval grounding, tighter tool permissions, improved evaluation harnesses, and clearer human-in-the-loop processes. Regular review keeps systems aligned as models and usage patterns evolve.

Teams operating generative AI or agent-based workflows can benefit from targeted evaluation. GlobalNodes AI Audit Services include generative AI system audits that evaluate LLM-based systems, AI agents, conversational workflows, and retrieval pipelines.

Generative and agent systems require the same discipline of independent assessment as any other critical operational technology.